- So, as I mentioned in my intro there, we're gonna start with a bit of a discussion about kind of the commercial side of generative AI. All three of you are at companies where there is a very obvious kind of commercial component of this that is perhaps a lot more immediate than it is for some folks in the audience.

So, Dan, maybe let's start with you. You know, Adobe moved quickly to launch its generative AI tool, Firefly. Talk to me about the scale of the business opportunity here for Adobe. And, you know, how much is this an opportunity for Adobe to drive additional revenue versus an expectation of your customers that there will be generative AI plugins to what Adobe offers?

- Yeah, and I appreciate the question and the reference to Firefly. It's not even one year old. I think later this month it turns one. But I think the opportunity is massive. As you think about the three areas we as a company are focused on, unleashing creativity for all, accelerating document productivity, empowering digital businesses, all three are gonna see a significant, meaningful tailwind as a result of this technology inflection.

And let me just take a step back and share a little bit of why I have so much conviction in it. As you look at major and technology inflections as they shape end markets, innovation goes up, economic growth goes up. Adobe's got a 40-year track record of being a key catalyst to many of the trends that shape the digital economy.

And there's a difference between capitalizing on a trend and shaping, being a key catalyst of that trend. We've got a long track record of doing it. This inflection's gonna be no different. And as I think about the AI stack, there's data, there's models, and then there's the products and workflows that bring these technologies to life.

The third of the three is the most relevant importance. That's where the magic happens with these technologies because it's only in the context of those products and workflows that this technology leads to real productivity enhancement. And so we're incredibly bullish about this technology and we fully intend to be a key catalyst in shaping our end markets and driving success for our customers.

- So one of the questions for, I imagine for all three of you on this panel, but then we're gonna stay with you on this for a moment, is sort of like how to commercialize this work. If the layer that you're thinking about is like, what are the products that sit below the data in the language model, how do you kind of commercialize those?

Adobe has these kind of generative AI credits that it's launched. So talk to me a little bit about how you kind of arrived at that as a decision. I think this is in the context of a lot of companies kind of scrutinizing how much they spend on software and services and so on.

And so the commercialization piece I think is fascinating and pitching it at the right level. - Yeah, what's great about the way we're positioned is we get to help our customers with both top line growth and bottom line productivity. So there's a uniqueness of how we engage with our customers that leads to that simultaneous benefit, which we really like.

Generative credits is one of a number of vectors that we're driving from a commercialization standpoint. As you think about the accessibility of our tools and products, making them more approachable and accessible. And as we think about customers engaging with the technology, time to success, how quickly they can be productive is an important component of long-term retention.

So more people top of funnel through more accessible technology, deeper level of engagement leading to better retention. And then as we engage with custom models, adding more value to the relationships we already have. Generative credits is one of a number of vectors that are available to the company. And as we think about deep power users of our products and our technology, when you tokenize engagement, turn a text into an image or a video or audio, those power users are consuming the technology at a differential rate.

This allows them, based on that enhanced productivity, to make sure that the value that they realize is commensurate with the economics. And so there's a usage-based consumption model that sits alongside the subscription that's tailored to the power users that gets tapped into as they derive value from the technology.

- Got it, okay. So Junior, I'd love to bring you in. ServiceNow launched its generative AI offering, I think in September, is that right? - Yeah. - That's right. - So you just reported on kind of the first quarter since it launched. So let's just start with kind of what is it and what has the uptake been since it launched?

- Yeah, so we've been actually, ServiceNow's been investing in AI for years and years. We actually had our first AI SKUs launched back in 2018. That was more predictive AI. And we saw pretty great uptick, both on pricing as well as penetration in our user base. So fast forward to September, we launched NowAssist, which is our generative AI SKUs.

We launched them on September 30th, so one day left of the quarter. And we actually had several multi-million dollar deals just in that one day. Q4 was the first full quarter of the launch. And it was by far our best performing new product launch ever to date. And so the opportunity is vast.

And it's all about really driving value for customers. What we've done is we've infused human-like conversational interfaces throughout the platform and it's about getting the users, whether they're employees or customers, the information they need much faster and more reliably and more accurate. And then it's about getting developers more productive.

And so we're seeing an uptick across the board, across the platform portfolio. And I think Gartner estimates that three trillion is gonna be spent on AI in the next, until 2027, with a third of that going to generative AI. So the opportunity is here and it's now. And we're seeing lots of interest from customers across the board.

And it's always about value add, right? And so how are customers gonna get to value more quickly? When we think about that, we try to leverage 10% of the value internally in pricing and give 90% of the value to customers. And so those conversations are extremely powerful and there's not one CFO or CEO that I talk to that is not really leaning in to what gen AI can do for their business because they believe, and I completely agree with them, that it's not only a productivity win and a cost savings win for them, but it's business model innovation, it's top-line growth.

And so we're really excited about the use cases for ServiceNow. - How do you differentiate what ServiceNow offers? All of your peers are coming out with generative AI offering and I'm sure everyone in this room gets pitched something generative AI related, frankly, all the time, probably twice on Sundays.

So how do you differentiate it and say, ours is the real deal? - Well, our strategy right now I think is unique in that we are focused on domain-specific large language models within the ServiceNow platform already. So it's customers or new customers that have already worked with the platform, understand the platform.

And so if you're working on ServiceNow to have that incremental capability and that incremental value add, if you think about it, it's like what CFO is gonna say, I don't want AI, I just want that standard SKU. No, they want the power and it's within the ServiceNow platform. So all the great workflows, all the great abilities to scale your operations, it's already there and it's already in the business.

And so other companies are doing the same within their platforms to supercharge. Back to your question initially, how much is us leaning in and believing in the top line or customers pulling for it? It's both, like customers are going to, if they're not asking for it already, they will be very, very quickly.

So those companies and those platforms that are building it in have got to really pick up pace and do it faster. Okay, so Anne-Marie, I'd like to bring you in. Tell me a little bit about how Airtable is thinking about this from a commercial standpoint and then we'll move the conversation on to sort of generative AI for the internal finance function.

Yeah, absolutely. So we're really excited about how to leverage GenAI within our existing platforms. We're a low-code platform. We already work with 80% of Fortune 100 companies to enable their knowledge workers, citizen developers, to create apps for their day-to-day work and their workflows. It's a really natural point to insert generative AI, which is very use case specific, integrated into the data and the workflow that already exists.

And so it's basically making things faster, making things easier, allowing GenAI to use the context of all the data more so than a human can. And we're thinking of it as, the big kind of functional use cases for us are marketing, product, sales, operations. And so we think of Airtable as sort of the co-pilot for all those operations, just like our engineering teams are using GitHub co-pilot and developers are probably the first in really getting efficiencies on GenAI, but we see that happening across these other functions.

And I can give you sort of like a internal and customer example of how that comes to life and our digital product team, as you can imagine, is most excited about this. And they've been ingesting all of our customer feedback using GenAI, which means reams of data from Salesforce, from Gong, from web.

GenAI then categorizes it by topic, sentiment ranks it sort of minus five to plus five, puts it in prioritization of what will make the biggest impact for customers, literally sort of responds with its own ideas of what product could we create to answer that feedback. How would we describe it?

What would we name it? Now, as you can imagine, it's really important to have humans in the loop on this sort of workflow automation. And so we believe strongly in like human creativity and judgment, it has to be there. And then GenAI can be there for enhancing scale and just sort of machine scale context gathering.

And then our product team literally then attaches that into the marketing workflow where they're generating messaging and ad copy emails that get better open rates and conversion. So that's our internal enthusiasm on our dogfooding. Externally, we have one of our really large cloud customers. They already use us for campaign management on the marketing side.

They're now using our GenAI pilot to generate ad copy, depending on what the creative brief is, what past performance is. They're finding existing marketing assets that otherwise wouldn't have been tagged to this specific initiative. So it reduces content waste. And so I sort of have been thinking of it as this really exciting, like win, win, win, win.

You save time, you save cost, you improve revenues by faster speed to market, better conversion. And then really powerfully, our employees just seem happier 'cause you're taking away the tedious tasks and the manual tasks, giving them more powerful content. And then their creativity and judgment is still the primary focus.

And I think it improves retention and satisfaction. And for engineering teams, that's a really big deal as a CFO to see engineers happy as a software company. - Sure. - It's been really exciting. - Sure, so we'll come back to the human in the loop piece 'cause I do want to kind of discuss that in a bit more detail.

But first, I'd like to focus a little bit on sort of generative AI for the finance function specifically. And so Gina, I'd like to start with you. You told me on our prep call last week that you're especially excited about using generative AI for sort of revenue recognition. That was one of the examples that we discussed.

So talk to me a little bit about that. Is that something that you're currently piloting? And if so, what have the results been so far? - Yeah, so I think there's numerous use cases within finance and we're piloting a whole bunch of them. We had a hackathon and we picked like the top 10 areas that we want to focus and we're going with them first, but we have a list of use cases.

P&A is a big area, automated forecasting. How do you forecast like a top line or a bottoms up and what's better? So we're doing a ton of that. But the revenue recognition piece is super interesting. If you think about it, it's all based on complex contract terms, right? And so the sales organizations ping finance between 3,000 and five times a quarter on just trying to understand the best way to formulate the contract to get the right rev rec.

And how do we use Gen AI to automatically answer? So many of them are similar questions and how do we automate that? And by the way, not only is it automation time from a finance perspective, but then my sales teams are spending less time figuring out how to draft the contract and spending much more time with customers.

So I think it's not only a productivity perspective, but also how do I get my sales people spending more time with customers? It's gonna be a much better customer engagement. And so twofold is an area. We've been piloting it and it's going extremely well. The finance teams love it because again, to Amberine's point, it's about automating the rotes and the routine and really giving time back for much more complex, interesting work.

And we're having lots of engagement across the board from our finance teams and really leaning in to how Gen AI is gonna simplify what they do every day. And they're so excited about it. - And are there any concerns about sort of the accuracy, the accuracy of a use case like that?

Let's say whatever model you're using is 95% accurate. We talked about having a human in the loop, but what has the pilot revealed in that regard? And what kind of safeguards have you put in place to make sure you're capturing that 5% of concern? - Well, I think the point is that the human interface is super important, right?

There's always gonna be a check on the backend as to does this contract term meet what it needs? And so there's going to be that review, but it's that interaction, that back and forth email exchange. Do you understand getting on the phone? Do you understand that? That's able to really be automated.

So you're 100% right. Accuracy is really important, but that human interface and interaction is extremely important. And by the way, CFOs, I don't know if you've gotten the rep in your rep letters yet. A lot of the big four accounting firms are repping, asking you to rep to make sure that if you're using AI, that there's that backend kind of closed loop around it.

So really important, it's a great point, but it's not to say that we shouldn't be afraid of using AI because of that. You just need to manage around it, just like we've managed around other technology evolutions in the years that we've been CFOs. - Sure. Dan, similarly, you held a kind of a finance team hackathon, which I think you said you generated about 100 ideas of sort of specific use cases.

As I understand it, you're moving forward with five pilots, is that right? So I'd like to focus on one today, which was, you've called it kind of the forecasting engine. So I think this touches a little bit on Gina's point about sort of FP&A and so on. So tell me again, how will this work?

And ultimately, what is the sort of end goal that you think is attainable and how far away are you from reaching that? - Yeah, and before I go into the detail, just about the hackathon, Gina was talking about it, I was talking about it. We were commenting backstage on the enthusiasm that we see in our organizations around this technology inflection.

Part of transformation is about change management and they're inextricably linked. How do you build that groundswell of enthusiasm within the organizations to lean into this technology inflection? You bring them into the process and you help them architect the solution and the way of working. So a hackathon is a really great way to build that groundswell.

Yeah, we had over 100 submissions. We boiled it down to 20. Five of them were prioritized, but now we have a living, breathing pipeline of future opportunities that we're gonna continue to focus on. So one of them is a forecasting engine. So like most companies, the annual planning process is a heavy lift inside of the company and we are in the process of pivoting to a more fluid environment, a rolling forecast environment.

But the way we communicate with investors in the street, that annual process is gonna be a thing. Now the question is, is how do you pivot to a rolling forecast and still maintain discipline from an annual standpoint? You communicate with investors. When you synthesize all of the data that sits inside of the company, you weigh it appropriately, you backtest it to make sure that the model and the algorithm that you tune accurately reflects the performance of the company, you can get to a rough cut annual plan very, very quickly.

I mean, this is a month and a half process that you can start to access a first rough cut approximation within minutes. We're early in the process. We did a, let's see what this spits out from a prior year and it was probably 85, 90% of what the teams ground through and it was done within minutes.

So it's a really powerful tool to sit alongside the planning process and the value in my view of a financial organization isn't about rolling up numbers. That's obviously important. Integrity of the numbers is super important but when do you see a signal and what's the insight from that signal?

When do you take action based on that insight and how long does it take you to drive impact? There is a speed of execution that comes with this technology because the time to signal and time to insight is gonna get sped up dramatically and then we take the great people that call our company home and turn them on to an action-oriented, solution-oriented mindset to solve business problems and having that forecasting engine sit alongside an annual planning process is an important aspect of focusing on if we do nothing, this is the likely outcome.

Now, what are the do different leads to get a different outcome? You transition to solution space very quickly and it's not just an annual planning process. We've got these predictive engines running alongside intra-quarter performance and it's fluid. I get input from the sales team on what they think they're gonna do and I've got my predictive engine and it's like, well, we disagree on that and we disagree on that and what actions are we gonna take to shape the outcome, to get where we need to be for the quarter and then we take a look at it at the end of the quarter and the accuracy embedded in this, if it's done well, is pretty impressive.

- How does that change your, the skillset required of you as the CFO? If you have this information, how does that change the way that you then pass that information out to the rest of the C-suite, the rest of the company, use that to potentially influence decision-making and so on?

Essentially, is this a kind of a powerful new decision-making tool that you have, you're the kind of C-suite owner of and so how do you cascade that out? I guess that's a question for all three of you, honestly, but let's start with you, Dan. - Yeah, so from my point of view, everybody's gonna have their own philosophy on how to do this role and do it well.

From my perspective, I think the role of finance CFO, sort through complexity, get the core underlying root cause drivers, frame the debate and dialogue, frame the decision-making process, sharpen business decision-making and once decisions are made, go drive execution and drive impact inside of the company. And so this is a key tool that gets us, again, to that signal and insights faster.

But there's a democratization of data. I don't think this sits in a finance silo. It's how you permeate this information to help shape that discussion, real-time business discussion inside of the company to get to better business decisions and better business outcomes. It's pervasive. - I would just add that the role of the CFO has been evolving for years now, right?

And this is just one area in addition. So the point that Dan was making, it's not about just having a forecast number quicker, which is super powerful. It's what you then do with the time that you have, that information earlier. You know, I talk about culture of finance. I want the finance organization to be the first call of the business.

It's not just about compiling the data and reporting out on it. It's about really driving impact and driving, okay, here's that signal. This is what we need to be doing. And by the way, we've been doing this for a while now, right? So we have, Dan, early insights into a quarter.

So if things in certain areas look like that they're not shaping up the way we thought, get in there early. And it's not just about telling the salesperson or telling the marketing person. It's about working with them jointly to go solve it, thinking really throughly, thinking strategically about what this information is able to drive.

And so I think the role is completely evolving. And we are probably the only person in the C-suite, besides the CEO, who has that bird's eye vantage across the enterprise that can really help drive impact in any area. And so, you know, oftentimes back in the day, I'd get my hands slapped to say, that's not finance, right?

That's not finance. This is mine. Now, they're like, Gina, bring it, right? They're like, they want you involved 'cause they know that you have the information that's gonna help them solve their problems and get to the better results. And so that role has been evolving for a while. And Gen AI is only gonna continue that evolution.

- Sure, and Marie? - Yeah, exactly what Gina said. I mean, we are in such a privileged position where we see absolutely everything. And I think that gives a lot of power, also a lot of responsibility. What I also think is super helpful is every one of my colleagues knows that I have relatively the most pure intentions of doing the best thing for the business and always thinking about the business and the customers.

And so while different functional groups, depending on the quarter, depending on the year, have different priorities where they're very focused on one thing, as finance, we're focused on the kind of long-term success of the business as a whole. And I think that just adds to the power of the voice, to the direction we give.

And then in terms of your earlier question of use cases within finance, we have a lot of parallel experiences, revenue recognition, forecasting. I'd say one of my teams, which has become a bit of a hero and usually isn't, is our accounts receivable team because we've been able to use Gen AI in a lot of different ways for them.

So content generation in terms of emails and follow-ups, chat in terms of interfaces with salespeople and customers, as well as just forecasting on cash collection times, prioritizing the risk of customers. And they've been really excited and it's just been nice to see a team that usually doesn't see the limelight pop up through this technology.

- Couldn't agree more. It's nice to hear that our use cases are very aligned. - Yeah, I would say, just on your point, Amber, we had a CIO network summit a couple of weeks ago and the CIO of Cisco was on one of the panels and he shared a similar anecdote to yours, but from an HR context where he said the response to recruitment emails that they send out is significantly higher when they're generated by a generative AI tool as opposed to by a human because it sounds counterintuitive, but it's more personalized using the generative AI tool.

- It's not cut and paste, right? - Yeah, yeah. But I'm curious, for this panel, with some of these use cases that we're talking about with finance specifically, right now, generative AI is quite expensive, whether it's the commercialization of the models, the licensing, enterprise license for the model itself, whether it's the data that is required to be fed into that, whether it's the human time required on the front end to get these things up and running.

So how are you thinking about the cost of some of these use cases that you're thinking about internally and how long they'll take to pay off? The value of what you've just described is clearly transformational in theory. Are you there yet? And how do you weigh cost versus return?

- I'll start there because I think it's a really important conversation. A lot of CFOs are all about ROI and it's super important, right? And it's about how quickly am I gonna get the productivity and what does it mean for cost savings? I think this is a whole different ballgame.

And I think you've got to invest upfront because if you don't, you're gonna be left behind. And how I think about it, and it's really hard to kind of pinpoint attrition, for example, but we go back to the employee engagement perspective, and this is not just in finance. This is across the board in the organization.

But when employees are doing more creative, more impactful work, they're much more engaged, which means attrition is lower and you get to, which is expensive from a cost perspective. But even more importantly than the cost, it's keeping that brain trust internal and inside. That is invaluable. So you've gotta be able to take the numbers, but also take the opportunity cost of not doing it into account when you're really thinking ROI.

And that's across the board, whether it's a finance or whether I'm selling to a customer when I'm talking to customers. It's about the value add and the productivity, but it's also about what happens if I don't invest today? One of the first acquisitions I greenlit when I joined in 2020 was a company called Element AI.

And it was about hiring talent in the AI space that didn't really have great ROI in the initial years. But at the end of the day, infusing AI into the platform was gonna be more and more important as we went on. And so we greenlit it, and it's one of the reasons why we were able to be first to market with actual Gen AI product in market.

And so as CFOs, you need to be a little bit more open today I think than maybe in times past of really investing in front of the curve. You need to be smart. It's about taking balance risks and saying yes to a few things potentially, but really making sure that you're not just hamstrung by the numbers only.

Because I think in this day and age, not investing is really gonna put you in trouble for the mid and long-term. - Anne-Marie? - Yeah, so you mentioned this earlier, Ben. Over the course of the last few years, we had a lot of tool sprawl and way too much technology spend.

So we have a tech council, and they see their role as primarily figuring out how to consolidate tools and reduce spend. We didn't wanna put AI into that council because the mandate of that council is very focused on cost savings. And so we created a separate AI council, which is kind of mandated to go find the most interesting value-added use cases and sort of on the four wins philosophy.

We want use cases where we get all four of those things, like time-saving, cost-saving, revenue improvement, morale, inspiration for the employees. And that AI council is looking at the world differently and surfacing different use cases. And I review those every kind of week or two weeks. And I'll often come in and be like, "Why did we say no to this?" And the answer will be like, "We weren't sure about the cost versus value." And then we'll talk through, "Are we underestimating the value "to make sure that we push on that use case?" I think when it comes to commercialization as buyers of software, we're seeing, there's a lot of evolution.

Like we're seeing some vendors price at zero, others price at like 80% of their base product. And I think everyone's trying to figure out exactly where the value equation shakes out. What will the take rate be at different pricing? So I think there's a lot to be learned about the evolution of what will this cost us over time.

- Got it. - It's an interesting point though, what you bring up. And we're hearing this a lot because we get the question, I'm sure you do Jen as well, of how are IT budgets being allocated to Gen AI versus the other pieces? And while you're right, they're definitely focused on cost takeout because of the application proliferation that we've seen.

It's one of the reasons why you're seeing a lot more platform consolidation. Like instead of having best point solutions in different areas, which by the way is very costly and from a security perspective, a nightmare, to platform consolidation, which really enables, I think, an interesting way of how CIOs are thinking about their budgets and spend.

It's like you're being mindful of costs here, but taking some of that savings and allocating it to the Gen AI and the modernization and the IT of the future. And I think that's what we're seeing when we're talking to customers. I don't know, Dan, if you're seeing the same.

- Yeah, 100%. I agree with everything Gina and Amberine just said, and I'll augment it, build on it with two comments. Change is coming. We have a choice. We can lead or we can follow. We can lead or it can be imposed upon us. I know where I am on that equation, but change is coming.

Second thing, not everything that can be counted counts and not everything that counts can be counted. And so this is one of those periods of time where we're gonna have to put on our comfort hat with ambiguity and be smart about the choices we make. Prioritize, focus on business impact, super important.

- And be able to pivot, and be able to pivot. If something you made a bet on is not coming to fruition, pivot fast and go to the next one. - We're going to open up to audience questions. I think we'll have time for one audience question. If anyone has one, get your thinking caps on.

But I guess my follow-up question from what you're just describing is like, how do you make sure that you don't over-commit? How do you make sure that in a year, two years time, you don't turn around and go, that really was quite expensive. Yeah, any thoughts on that? You're all very enthusiastic about this, but there must be a ceiling.

- Yeah, so and I mean it in all sincerity. We see it on our product teams. Hyper-focused from a prioritization standpoint on what really is going to move the needle. That's the external facing aspects of our roadmap. The stuff is going to be expensive to invest in. There's going to be a dynamic where that line is drawn.

What is below the line today may have been above the line yesterday. Be focused and prioritize. We can't do 100 things well. We're doing five, we're taking a journey. We're going to learn a lot in this process. The five that we're initially doing are solving real business impacts. You see the pull through from the business, our business partners, and we're not getting out over our skis.

We are moving fast and we're being aggressive, but we're not getting out over our skis. And eventually you're going to develop this flywheel inside of the company where you can do more and more and develop momentum, but it's going to be based on proof points and real experience as opposed to discussions and conversations.

- The only thing I'd add to that just very quickly is prioritization 100%. As CFOs, I think we have a really important seat at the table to help the company prioritize. Because again, we see cross-enterprise. The things that are going to be most impactful are on the list first, and you just keep plowing down the list.

But we have that responsibility to help the company prioritize. Otherwise, because everyone's pulling. Everyone wants AI, marketing, finance, legal. How do we really focus on what's the most meaningful? I think CFOs have a real role to play there. - Yeah, and to build on that point, we have internally our North Star is anything that helps our own product development on AI gets priority.

And so we haven't introduced our officials Q yet, but we already had sales in Q4 because customers were asking for it. So any investment we make into our teams and developing our own product helps our customers, helps the business. So we'll prioritize that over some of the internal efficiency maybe, and then iterate and learn and make sure, like we're not gonna wait for three years to see what happened.

Like every quarter we'll be iterating and pivoting. - Got it, okay. Do we have any questions from the audience? If so, raise your hand. Okay, thank you. Oh, yes, we have one here. Microphone is just making its way towards you. If you could just state your name and company, please.

- Great insights. Thank you for the discussion. My name is Guru Ramamurthy, I'm from Bayer. Some of you mentioned earlier on in the conversation about not just this being a cost and productivity game, but also revenue. When you look at your clients and the products you're offering, how does it actually translate into revenue?

Because from where I sit, this feels and sits more like a cost and productivity savings initiative, albeit in a supercharged environment. Can you articulate a little bit on the revenue side, please? - I'll give one. So we announced a partnership with Visa just last quarter. And what Visa is partnering with us on, with Gen AI, is all about building a solution to automate disputes for all the banks that issue Visa cards, for example.

And so the ability for them to monetize that solution is real, and not only will that generate revenue for them, from a disputes perspective, the savings on just being able to resolve or identify issues much earlier in the process is huge. Billions is what they're talking about. And so that's just one example of how Gen AI is going to help our customers, not only from the bottom line, but also from the top line.

- Yeah, and on the marketing side, we've already seen improvement in open rates on marketing messages, conversion rates on top of funnel. So that's one avenue. Closer to your use case lately, we have a life sciences customer who's using Gen AI to collate research, comb through research faster, and they believe it'll get them to market faster in order to be able to do both faster drug discovery, as well as get drugs on the market faster.

So I really do think we'll see true revenue evolution from this. - There's a lot of interesting use cases for health. - Yeah, I'll throw out another example. IBM just came public with this statistic in the last 24, 48 hours. They've standardized on our technology from a content production and distribution.

They're seeing 10x increase in productivity. They're serving that content up to their customers 60% faster and the content that their customers get, they're engaging 26x. A lot of it rests on those digital insights, being able to sort through the complexity of that customer data, productivity on the front end from a content creation standpoint, and activation and delivery, the insights that are fueling those types of statistics for a very large, complex company.

This stuff is real for those that are leaning into it and leaning in in the right way. - We're over slightly by a minute or two, but Dan, I had a final closing question for you. It's slightly unrelated to the topic of this session, but one of the themes that we're exploring at this event is the outlook for M&A.

It's obviously been a quiet period for M&A broadly. Some of that has been due to concerns about antitrust activity. Adobe recently sort of walked away from its planned acquisition of Figma. What did you learn from the process and given the sort of antitrust environment, is Adobe still in a position to be opportunistic or are you planning on sort of wait and see mode for a little bit?

- Well, the great thing is for all the reasons we've been up on stage talking about, we've got an enormous set of opportunities in front of us and we couldn't be more excited about what's in front of us. When I take a step back philosophically about what it means to sit in seats like this, the CEO seat, we plan a decade out.

We think about what it's gonna take to win and win big in our core markets, to be a leader, to inflect those markets, to be pervasive in the way in which our customers get work done. Regulatory environments ebb and flow. Strategic decision-making of enterprises doesn't. And so nothing changes as a result of an ebb and flow in the regulatory environment.

We're gonna do what we've always done, which is drive a strong, organic engine of innovation. We'll complement it from time to time with inorganic activity, but we're gonna be thinking long-term what it takes our company to win. We find an opportunity, we're gonna go action it and we're not gonna try to be prognosticators of the ebb and flow of a regulatory environment.